Abstract
In any research project involving data-rich assays, exploratory data analysis is a crucial step. Typically, this involves jumping back and forth between visualizations that provide overview of the whole data and others that dive into details. In data quality assessment, for example, it might be very helpful to have one chart showing a summary statistic for all samples, and clicking on one of the data points would display details on this sample in a second plot. Setting up such interactively linked charts is usually too cumbersome and time-consuming to use them in ad hoc analysis. We present R/LinkedCharts, a framework that renders this task radically simple: Producing linked charts is as quickly done as is producing conventional static plots in R, requiring a data scientist to write only very few lines of simple R code to obtain complex and general visualization. We expect that the convenience of our new tool will enable data scientists and bioinformaticians to perform much deeper and more thorough EDA with much less effort. Furthermore, R/LinkedCharts apps, typically first written as quick-and-dirty hacks, can also later be polished to provide interactive data access in publication quality, thus contributing to open science.
Competing Interest Statement
The authors have declared no competing interest.
